CVA file: an index structure for high-dimensional datasets

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An, Jiyuan
Chen, Hanxiong
Furuse, Kazutaka
Ohbo, Nobuo
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2005
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Abstract

Similarity search is important in information-retrieval applications where objects are usually represented as vectors of high dimensionality. This paper proposes a new dimensionality-reduction technique and an indexing mechanism for high-dimensional datasets. The proposed technique reduces the dimensions for which coordinates are less than a critical value with respect to each data vector. This flexible datawise dimensionality reduction contributes to improving indexing mechanisms for high-dimensional datasets that are in skewed distributions in all coordinates. To apply the proposed technique to information retrieval, a CVA file (compact VA file), which is a revised version of the VA file is developed. By using a CVA file, the size of index files is reduced further, while the tightness of the index bounds is held maximally. The effectiveness is confirmed by synthetic and real data.

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Knowledge and Information Systems

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7

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3

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Data Structures

Artificial Intelligence and Image Processing

Information Systems

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